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@article{Baringhaus2004,
	Author = {Baringhaus, L. and Franz, C.},
	Date-Modified = {2014-04-01 18:44:29 +0000},
	File = {:Users/smorris/Dropbox/Mendeley/Journal of Multivariate Analysis/Baringhaus, Franz - 2004 - On a new multivariate two-sample test.pdf:pdf},
	Issn = {0047259X},
	Journal = {Journal of Multivariate Analysis},
	Keywords = {bootstrapping,multivariate two-sample test,orthogonal invariance,projection method},
	Month = jan,
	Number = {1},
	Pages = {190--206},
	Title = {{On a new multivariate two-sample test}},
	Volume = {88},
	Year = {2004},
	Bdsk-Url-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0047259X03000794},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/S0047-259X(03)00079-4}}

@article{Gneiting2006,
	Author = {Gneiting, Tilmann and Larson, Kristin and Westrick, Kenneth and Genton, Marc G and Aldrich, Eric},
	Date-Modified = {2014-04-01 18:44:29 +0000},
	File = {:Users/smorris/Dropbox/Mendeley/Journal of the American Statistical Association/Gneiting et al. - 2006 - Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center.pdf:pdf},
	Issn = {0162-1459},
	Journal = {Journal of the American Statistical Association},
	Month = sep,
	Number = {475},
	Pages = {968--979},
	Title = {{Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center}},
	Volume = {101},
	Year = {2006},
	Bdsk-Url-1 = {http://www.tandfonline.com/doi/abs/10.1198/016214506000000456},
	Bdsk-Url-2 = {http://dx.doi.org/10.1198/016214506000000456}}

@article{Gneiting2007,
	Abstract = {Scoring rules assess the quality of probabilistic forecasts, by assigning a numerical score based on the predictive distribution and on the event or value that materializes. A scoring rule is proper if the forecaster maximizes the expected score for an observation drawn from the distributionF if he or she issues the probabilistic forecast F, rather than G ≠ F. It is strictly proper if the maximum is unique. In prediction problems, proper scoring rules encourage the forecaster to make careful assessments and to be honest. In estimation problems, strictly proper scoring rules provide attractive loss and utility functions that can be tailored to the problem at hand. This article reviews and develops the theory of proper scoring rules on general probability spaces, and proposes and discusses examples thereof. Proper scoring rules derive from convex functions and relate to information measures, entropy functions, and Bregman divergences. In the case of categorical variables, we prove a rigorous version of the Savage representation. Examples of scoring rules for probabilistic forecasts in the form of predictive densities include the logarithmic, spherical, pseudospherical, and quadratic scores. The continuous ranked probability score applies to probabilistic forecasts that take the form of predictive cumulative distribution functions. It generalizes the absolute error and forms a special case of a new and very general type of score, the energy score. Like many other scoring rules, the energy score admits a kernel representation in terms of negative definite functions, with links to inequalities of Hoeffding type, in both univariate and multivariate settings. Proper scoring rules for quantile and interval forecasts are also discussed. We relate proper scoring rules to Bayes factors and to cross-validation, and propose a novel form of cross-validation known as random-fold cross-validation. A case study on probabilistic weather forecasts in the North American Pacific Northwest illustrates the importance of propriety. We note optimum score approaches to point and quantile estimation, and propose the intuitively appealing interval score as a utility function in interval estimation that addresses width as well as coverage.},
	Author = {Gneiting, Tilmann and Raftery, Adrian E.},
	Date-Modified = {2014-04-01 18:44:29 +0000},
	File = {:Users/smorris/Dropbox/Mendeley/Journal of the American Statistical Association/Gneiting, Raftery - 2007 - Strictly Proper Scoring Rules, Prediction, and Estimation.pdf:pdf},
	Issn = {0162-1459},
	Journal = {Journal of the American Statistical Association},
	Keywords = {bayes factor,bregman divergence,brier score,coherent,continuous ranked probability score,cross-validation,distribution,entropy,kernel score,loss function,minimum contrast estimation,negative definite function,prediction interval,predictive,quantile forecast,scoring rule,skill score,strictly proper,utility function},
	Month = mar,
	Number = {477},
	Pages = {359--378},
	Title = {{Strictly Proper Scoring Rules, Prediction, and Estimation}},
	Volume = {102},
	Year = {2007},
	Bdsk-Url-1 = {http://www.tandfonline.com/doi/abs/10.1198/016214506000001437},
	Bdsk-Url-2 = {http://dx.doi.org/10.1198/016214506000001437}}

@article{Grimit2006,
	Author = {Grimit, E. P. and Gneiting, T. and Berrocal, V. J. and Johnson, N. a.},
	Date-Modified = {2014-04-01 18:44:29 +0000},
	File = {:Users/smorris/Dropbox/Mendeley/Quarterly Journal of the Royal Meteorological Society/Grimit et al. - 2006 - The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble.pdf:pdf},
	Issn = {00359009},
	Journal = {Quarterly Journal of the Royal Meteorological Society},
	Month = oct,
	Number = {621C},
	Pages = {2925--2942},
	Title = {{The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification}},
	Volume = {132},
	Year = {2006},
	Bdsk-Url-1 = {http://doi.wiley.com/10.1256/qj.05.235},
	Bdsk-Url-2 = {http://dx.doi.org/10.1256/qj.05.235}}

@article{Szekely2013,
	Author = {Sz\'{e}kely, G\'{a}bor J. and Rizzo, Maria L.},
	Date-Modified = {2014-04-01 18:44:29 +0000},
	File = {:Users/smorris/Dropbox/Mendeley/Journal of Statistical Planning and Inference/Sz\'{e}kely, Rizzo - 2013 - Energy statistics A class of statistics based on distances.pdf:pdf},
	Issn = {03783758},
	Journal = {Journal of Statistical Planning and Inference},
	Keywords = {Distance correlation,Distance covariance,Energy distance,Goodness-of-fit,Multivariate independence},
	Month = aug,
	Number = {8},
	Pages = {1249--1272},
	Publisher = {Elsevier},
	Title = {{Energy statistics: A class of statistics based on distances}},
	Volume = {143},
	Year = {2013},
	Bdsk-Url-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0378375813000633},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.jspi.2013.03.018}}

@article{Szekely2005,
	Author = {Sz\'{e}kely, G\'{a}bor J. and Rizzo, Maria L.},
	Date-Modified = {2014-04-01 18:44:29 +0000},
	File = {:Users/smorris/Dropbox/Mendeley/Journal of Multivariate Analysis/Sz\'{e}kely, Rizzo - 2005 - A new test for multivariate normality.pdf:pdf},
	Issn = {0047259X},
	Journal = {Journal of Multivariate Analysis},
	Keywords = {bhep test,goodness-of-fit,henze,multivariate,multivariate kurtosis,projection pursuit,skewness,strictly negative definite,zirkler test},
	Month = mar,
	Number = {1},
	Pages = {58--80},
	Title = {{A new test for multivariate normality}},
	Volume = {93},
	Year = {2005},
	Bdsk-Url-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0047259X03002124},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.jmva.2003.12.002}}

@article{Gneiting2011,
	Author = {Gneiting, Tilmann and Ranjan, Roopesh},
	Date-Modified = {2014-04-01 18:44:29 +0000},
	File = {:Users/smorris/Dropbox/Mendeley/Journal of Business \& Economic Statistics/Gneiting, Ranjan - 2011 - Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules.pdf:pdf},
	Issn = {0735-0015},
	Journal = {Journal of Business \& Economic Statistics},
	Keywords = {Continuous ranked probability score, Predictive ab,continuous ranked probability score,predictive ability testing,probabilistic forecast,proper scoring rule,quantile,weighted likelihood ratio test},
	Month = jul,
	Number = {3},
	Pages = {411--422},
	Title = {{Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules}},
	Volume = {29},
	Year = {2011},
	Bdsk-Url-1 = {http://www.tandfonline.com/doi/abs/10.1198/jbes.2010.08110},
	Bdsk-Url-2 = {http://dx.doi.org/10.1198/jbes.2010.08110}}
